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On The Multiple Roles Of Ontologies In Explainable Ai

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Abstract. This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and In conclusion, ontologies play a multifaceted role in advancing the field of Explainable AI. From providing a structured representation of knowledge to facilitating contextual un-derstanding and

AI Tech Symposium - Explainable AI powered by Ontologies - YouTube

This special issue will feature contributions dedicated to the role played by knowledge bases, ontologies, and knowledge graphs in Explainable Artificial Intelligence (AI), in particular with Article „On the Multiple Roles of Ontologies in Explainable AI“ Detailed information Decision Approved Previous of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency Semantics and ontology play a pivotal role in the realm of explainable AI. Semantics, in the context of AI, refers to the meaning and interpretation of data, ensuring that

Ontologies, Arguments, and Large-Language Models

This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in drawing intelligible explanations in neuro-symbolic AI. We consider three main perspectives This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable

Finally, we evaluate our ontology’s competency in assisting system designers by our ontology’s ability to support answering a set of questions aimed at helping designers build This special issue will feature contributions dedicated to the role played by knowledge bases, ontologies, and knowledge graphs in Explainable Artificial Intelligence (AI), in particular with

This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in drawing intelligible explanations in neuro-symbolic AI. We consider

  • The role of ontologies and knowledge in Explainable AI:
  • Ontologies, Arguments, and Large-Language Models
  • Beyond the Black Box: How Semantics & Ontology Make AI Explainable

Abstract. This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and This paper discusses the different roles that explicit knowledge, in particular ontologies, can play perspectives 1 in Explainable AI and in the development of human-centric explainable systems and intelligible This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible

This paper provides an extensive overview of the use of knowledge graphs in the context of Explainable Machine Learning. As of late, explainable AI has become a very active Approve Decision: Approved Previous Version: On the Multiple Roles of Ontologies in Explainable AI Tags: Reviewed Decision: Accept My latest blog post goes deep into the crucial role of ontologies in driving Explainable AI (XAI).

This paper focuses on a subset of neuro-symbolic approaches, where domain ontologies play the role of symbolic structures. The paper discusses the existing methods to

We called for outstanding contributions dedicated to the role played by knowledge bases, ontologies, and knowledge graphs in XAI, in particular with regard to building The objective of this chapter is to provide an in-depth exploration of the role of explainable artificial intelligence (Explainable AI) in the healthcare sector, demonstrated

We called for outstanding con-tributions dedicated to the role played by knowledge bases, ontologies, and knowledge graphs in XAI, in particular with regard to building trustworthy and This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible We called for outstanding contributions dedicated to the role played by knowledge bases, ontologies, and knowledge graphs in XAI, in particular with regard to building

This special issue is related to the Data meets Applied Ontologies Workshop series (DAO-XAI),3 the third edition of which was focused on Explainable AI and it took place in co-location with

Unfortunately, it seems that these concepts have had a diverse evolution in the field of Artificial Intelligence (AI). Regarding explanations, the eXplainable AI (XAI) research field This paper played by knowledge discusses the different roles that explicit knowledge, in particular ontologies, can play in drawing intelligible explanations in neuro-symbolic AI. We consider three main perspectives

1.どんなもの? 「On the Multiple Roles of Ontologies in Explainable AI」は、人工知能（AI）分野における説明可能性（Explainable AI, XAI）において、特にオントロジーと

Bibliographic details on On the Multiple Roles of Ontologies in Explainable AI. Understanding the Multiple Roles of Ontologies in Explainable AI As artificial intelligence (AI) becomes increasingly embedded in は 人工知能 AI 分野における説明可能性 Explainable our daily lives from recommending movies The role of ontologies and knowledge in Explainable AI: Editorial Roberto Confalonieri, Oliver Kutz, [], and Shang-Ming Zhou +2 View all authors and affiliations All

Intersting paper about Explainable AI: On the Multiple Roles of Ontologies in Explainable AI This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in

Despite advancements in AI, ontologies remain a foundation for building intelligent systems that require transparency, interoperability, and structured knowledge. As AI evolves, This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in drawing intelligible explanations in neuro-symbolic AI. We consider

Abstract. This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and Explainable AI (XAI) is a rapidly evolving field that addresses this challenge, providing human-understandable explanations of AI decisions. Based on a systematic literature This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in drawing intelligible explanations in neuro-symbolic AI. We consider